【作者】 刘念涛；

【导师】 刘希玉；

【作者基本信息】 山东师范大学 ， 计算机软件与理论， 2008， 硕士

【摘要】 由信息技术、网络技术的飞速发展所导致的“数据爆炸但知识贫乏”的现象日益严重,在这一环境下,数据挖掘应运而生并获得了快速发展,国内主流网站评比的未来十大热门技术中,数据挖掘技术占了一席之地。数据挖掘是一个多学科交叉的研究领域,涉及到数据库技术、人工智能、机器学习、统计学,知识获取、生物计算等多门学科的理论与技术,其发展必将大大影响全球信息化的进程,对其进行系统、深入、全面、详尽地研究是信息化发展的客观需要。而作为数据挖掘分析方法之一的聚类分析更是表现突出,在包括模式识别、数据分析、图象处理,以及市场研究等许多领域得到广泛的应用。本文对数据挖掘技术,尤其是聚类分析进行了较为系统的分析和研究,提出了一些想法和改进,主要包含以下内容:数据挖掘技术的概述。介绍了数据挖掘的概念,对数据挖掘的发现模式、数据挖掘的方法及应用进行了详细的分类、归纳和总结。聚类分析技术的概述。聚类是一种重要的数据分析技术,是数据挖掘理论框架中一个必不可少的部分,通过搜索并识别数据结合,从而描述数据,发现数据之间隐含的、有趣的相互联系。本文对聚类分析的定义、数据类型、聚类分析中的主要算法及度量标准作了简要的介绍和系统的学习。为本文的全面展开奠定了基础。蚁群算法的概述。蚁群算法是一种模拟群体智能的算法,在解决基于离散空间的问题时表现出良好的性能。本文由群体智能引出蚁群算法,并对蚁群算法的原理及特征做了简要的介绍。通过分析蚁群算法的优缺点,指出该算法的一点改进的研究方向。针对蚁群算法早熟、停滞及容易陷入局部最优等缺陷,借助蚁群算法与遗传算法相融合的思想并加以改进,将融合思想由解决离散域问题向更有效的解决连续域问题过渡,引出改进的基于交叉变异操作的蚁群算法,来提高蚁群算法的性能。提高性能的蚁群聚类组合算法的研究。在研究基本蚁群聚类模型的基础上,通过对公式、半径、空间分割的改进,借助于短期记忆的思想,提出了一种改进的基本蚁群聚类方法(ILF算法),通过改进,减少了参数的设置,降低了计算的时间,并具有较强的鲁棒性。结合K-means算法,提出了一种改进的基于信息素的K-means改进算法,通过对改进信息素的转移概率判断标准来进行聚类,减少了算法的参数个数,加快了聚类的进程。针对这两种算法的聚类特点,提出一种蚁群聚类组合算法策略,该策略首先利用改进的ILF算法来完成一次聚类过程,然后收集聚类结果样本,对样本数据再利用改进的基于信息素的K-means算法进行结果的后期优化处理,进行二次聚类过程,以此得到比单个算法更优的性能。最后对各种算法进行数据测试和性能分析,并把蚁群聚类组合算法用于银行信用卡客户的消费行为分析。更多还原

【Abstract】 The phenomenon about“the data explode but knowledge is poor”caused by rapid development of information technology and network technology is serious day by day. Under this environment, data mining was put forward as a kind of new technology, which demonstrated its strong vitality more and more.In some domestic primary website,data mining technology was chosen one of the most popular technologies in the future. Data mining is a research field of crossing multi-disciplines, and its development will influence the process of the global informaionization greatly. So it is the desirability of informationization development to have a research on data mining technology in an all-round and systematical way. Clustering analysis as one of data mining analysis methods is the performance is prominent, including the pattern recognition, the data analysis, the imagery processing, as well as the marketing research and so on many domains obtain the widespread application.This dissertation deeply studies and analyses the data mining technology, especially the one for clustering analysis. And then some ideas and improvements have been proposed. The main contents are listed as follows:Description of the data mining technology in brief. The clustering is one kind of important data analysis technology, which is an essential part in the data mining theory frame, through searches and distinguishes the data union, the description data, discovered between the data conceals and the interesting mutual relation.The basic concepts of data mining technology are introduced first. Based on the basic concepts of data mining, this dissertation classifies and summarizes the objects of data mining, the findable patterns , common method and application in detail.Description of clustering analysis in brief.The definition, data type, primary algorithms and mesaure standard are briefly introduced. All of the above become the basis for this dissertation.Description of the ant colony algorithm(ACA) in brief. ACA is a new-type bionic algorithm which comes from the resarch on the biological world of nature. It is a typical representative algorithm of Swarm Intelligence which have good performance for discrete space problem. This article draws out the ant group algorithm by the community intelligence, and explained the principle and characteristic of the algorithm in detail. Through the analysis ant colony algorithm good and bad points, point out this algorithm an improvement research direction. In view of the ant group algorithm precocious, the stagnation and easy to fall into partially the most excellent flaw, the thought fuse which with the aid of the ant group algorithm and the genetic algorithm ,which can solute separate territory question to the more effective solution continual territory question transition. Improved the overlapping variation operation ant group algorithm, which can enhance the ant group algorithm the performance.Enhances the performance the ant cybotaxis class combination algorithm research. Research on the algorithm on ACA, an improved LF algorihm is presented firstly, through change formula, radius, spatial division and with the aid of short memory. It has reduced the number of the parameters and the computation time. Unifies the K-means algorithm, an improved K-means algorithm based on the pheromone is introduced detailedly. The algorithm works with the transformation probability to realize the clustering. It has reduced the number of the parameters and improved the speed of clustering. According to two algorithm characteristics, an algorithm based on ACA and clustering algorithm combination is proposed, which include two clustering courses. The first is to accomplish the task of clustering by using ILF algorithm, the datum are collected and identified with the method SACA. At last, the result of clustering will be ptimized by the algorithm of K-means based on the pheromone inorder to get more superior performance compare to single algorithm.At last,combination algorithm is tested by some datasets and used to analyse bank credit card customer spending behavior.更多还原